In a blogpost updated personally by Lisa Huang, Twitter’s Senior Software Engineer for Search Quality explained that search results in reverse chronological order may not display “what the searchers are looking for”.

To cater cultivated search results, the team tapped into user’s behavioral data where both attributes of Tweets are shown alongside with consumer’s reaction.

This data is utilized by Twitter to train its machine learning models, allowing it to predict the probability of user’s engagements on a tweet. Once the predictions are done, it would then rank “relevant” Tweets based on the probability of engagement.

According to Huang’s observation, users who have experienced this new Search system have more engagement as in the users tend to Tweet more and spend more time on Twitter. Looking at such positive outcome, Huang and team indicate there will be more effort on expanding “algorithmically relevant tweets”.